You’re in a forecast review with your CEO, and it seems to be going well. She’s engaged, nodding along as you walk through the numbers. Then, she poses a simple question:
“What happens to EBITDA if we tweak our gross margin assumptions from 60% to 50%?”
Suddenly, the momentum stalls. Your model wasn’t built for on-the-fly adjustments. Changing that one variable means untangling layers of nested formulas, tracking down buried assumptions, and praying you don’t break something in the process. You tell her you’ll have to follow up after the meeting. The conversation continues, but the opportunity to stress-test assumptions in the moment has come and gone.
This is what happens when a model leans too far in one direction—either so complex that it’s unwieldy or oversimplified to the point of being useless. There’s no such thing as a perfect model, but the best ones balance depth and usability. They’re powerful enough to drive meaningful insights while being structured in a way that makes them easy to follow, modify, and scale.
Here’s a quick litmus test to see where yours stands:
- Can leadership tweak key assumptions without breaking them?
- If you reopened your model after six months, would it still make sense?
- If the person who built it left, could someone else pick it up and run with it?
If any of these questions make you uneasy, it’s time to rethink your approach.
Welcome to part three of our five part series on the pillars that define top-performing FP&A teams.
Let’s break down how the best FP&A teams find the sweet spot between simplicity and complexity.
The dangers of over and under-engineering models
Building a great FP&A model is a balancing act. Go too deep, and you end up with a brittle system that’s nearly impossible to update and understood by only a handful of people. Oversimplify, and the model loses its grip on reality, glossing over critical business drivers and leading to decisions based on flawed assumptions.
The risk of over-complexity
More detail = more accuracy. That’s the logic behind most over-engineered FP&A models. The problem with this framework is that it’s too simplistic—depth is great, but when it comes at the expense of usability, you start to run into trouble. A model packed with deeply nested formulas and edge-case logic might produce precise forecasts today, but what happens when you need to roll it forward? If every update turns into a time-consuming rebuild, that level of detail is working against you.
Consider a headcount forecast where every role has 20+ unique assumptions baked in: attrition rate by department, ramp time by geography, salary inflation by tenure, etc. In theory, it’s precise. In practice, it’s a maintenance nightmare. If leadership wants to test the impact of a hiring freeze, a simple tweak turns into an all-day project.
How do you know when complexity is tipping into inefficiency? Here are a few telltale signs:
- Only the creator fully understands how the model works, making it a single point of failure.
- Even minor updates require re-architecting key components.
- Stakeholders struggle to interpret the outputs, leading to a lack of trust in the numbers.
There’s no need to fear complexity. Great teams don’t shy away from it—they just make sure to balance sophistication with malleability. Models can be highly detailed, but they should also be relatively easy to update and scale.
The trap of oversimplification
On the flip side, some teams overcorrect in the name of simplicity, stripping their models down so much that they stop reflecting how the business actually operates. A streamlined structure is great…until it becomes so high-level that leadership starts second-guessing the numbers.
For example, a revenue forecast that applies a single growth rate across all customer segments might seem efficient, but if it doesn’t factor in churn, expansion revenue, or contract length variations, it’s not doing its job. A model like this leads to consistently inaccurate forecasts.
A model should be easy to use, but it also needs to be rigorous enough to support real strategic choices. The best FP&A teams don’t chase complexity, but they don’t cut corners, either. They focus on what moves the needle, building models that are as sophisticated as they need to be—no more, no less.
Here’s how to get there.
How to find the sweet spot between simplicity and complexity
1. Prioritize what moves the needle
Not every line item deserves the same level of scrutiny. The Pareto principle applies here: 80% of the impact comes from 20% of the inputs. That 20% deserves more time and energy than the rest.
For instance, applying a flat growth rate across all expense categories might seem like a clean approach, but it artificially flattens meaningful variability. At the same time, spending hours fine-tuning assumptions for a line item that makes up less than 0.5% of total spend isn’t time well spent.
So, what does the right approach look like? That depends on what your biggest financial levers are. Headcount typically represents a majority of costs and should be modeled with precision—factoring in hiring plans, attrition, and ramp times. Meanwhile, smaller expense categories, like office supplies, can be modeled with simple assumptions to keep things clean and manageable.
The goal is to focus your efforts where they’ll have the biggest impact while keeping everything else lightweight and scalable.
2. Keep the front end simple and the back end powerful
Great models are easy to navigate. If leadership can’t make quick adjustments without breaking something, it’s failing at its core purpose. No one wants to dig through 50 tabs just to tweak a single assumption.
The best models balance depth with usability. They provide the right level of detail where it matters while making it easy for decision-makers to interact with the numbers.
✅ A dashboard where the CFO can toggle revenue growth rates and instantly see P&L impacts.
❌ A model so fragile that changing one input breaks five dependencies.
The right level of complexity depends on the company’s size, industry, and stage. A startup might get away with applying a single growth rate across all markets, but a multi-product, multi-market enterprise needs more nuance. That doesn’t mean breaking down forecasts by every SKU, channel, and region—just the ones that materially impact performance.
3. Document assumptions and logic
We’ve all worked with a whiz who can build models in their sleep. But if they left tomorrow, could someone else pick up their model without hours of reverse-engineering?
Too many FP&A models become black boxes—assumptions hidden in deep-nested formulas, hardcoded inputs without explanation, and data sources scattered across multiple tabs. When no one knows how the numbers come together, trust in the model erodes.
The fix is simple: make documentation a habit. The best teams build “Read Me” tabs that spell out:
- Key drivers and assumptions: What’s influencing the outputs, and why.
- Data sources: Where the numbers are coming from and how frequently they update.
- Update instructions: What should be changed, when, and where to do it.
A simple version control log also goes a long way—tracking changes so updates are reversible and no one has to wonder, why did we adjust revenue assumptions last quarter, again?
4. Automate wherever possible
A good FP&A model should minimize manual work. If a task is being done the same way every month, it’s a sign that you could use some automation in your workflows.
For instance, instead of manually pulling CRM and ERP data, set up an automated sync. Rather than copy-pasting numbers into reports, use formulas and scripts to clean and process data. These small efficiencies add up, freeing up more time for high-value analysis rather than data wrangling.
Automation is a core pillar of building scalable, reliable models—but it extends far beyond financial modeling. In the next post in this series, we’ll dive deeper into how the best FP&A teams use automation to drive efficiency across forecasting, reporting, and decision-making.
5. Build for usability now and in the future
Think about the models your team has been using for years. Staff have come and gone, business priorities have changed, yet they remain intact. You don’t need to change or rebuild them because they just work—they’re easy to update, navigate, and provide the outputs leadership is looking for.
Durability is the hallmark of a great FP&A model. Too often, teams focus only on immediate needs without considering whether their model can stand the test of time. Then, as assumptions shift and business conditions change, the model crumbles…or becomes so bloated with patches and workarounds that no one trusts it anymore.
Here’s a general framework for creating models that are built to last:
❌ Not enough: A model that works today but falls apart with minor changes.
🤯 Too much: A model built to accommodate every edge case, making even simple updates a chore.
🎯 Just right: A scalable, flexible architecture that allows for quick updates without constant rebuilding.
The key to longevity? Thoughtful organization, clear documentation, and a model that’s intuitive enough to be picked up by anyone—whether it’s tomorrow or six months from now.
If a model only works for you, it doesn’t work
Ultimately, the true measure of a model’s effectiveness is how it holds up over time. If someone on your team reopens it in a year and it reads like a foreign language, or minor tweaks require hours of troubleshooting, complexity has outweighed functionality.
Top teams avoid this trap by focusing on usability from the start. Revisit the litmus test:
✅ If you don’t touch it for six months, will you (and your team) still understand it?
✅ If someone new inherits it, can they pick it up without a full debrief?
✅ Can leadership adjust key assumptions without breaking it?
If any of those give you pause, it’s time to simplify. The best models aren’t the most intricate—they’re the ones that are still being used years after they were built.
In the next installment of this series, we’ll break down how implementing automation wherever possible can turbocharge your team’s performance while slashing error rates.
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